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Inferring monetary policy objectives with a partially observed state

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  • Givens, Gregory E.
  • Salemi, Michael K.

Abstract

Accounting for the uncertainty in real-time perceptions of the state of the economy is believed to be critical for monetary policy analysis. We investigate this claim through the lens of a New Keynesian model with optimal discretionary policy and partial information. Structural parameters are estimated using a data set that includes real-time and ex post revised observations spanning 1965–2010. In comparison to a standard complete information model, our estimates reveal that under partial information: (i) the Federal Reserve demonstrates a significant concern for stabilizing the output gap after 1979, (ii) the model׳s fit with revised data improves, and (iii) the tension between optimal and observed policy is smaller.

Suggested Citation

  • Givens, Gregory E. & Salemi, Michael K., 2015. "Inferring monetary policy objectives with a partially observed state," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 190-208.
  • Handle: RePEc:eee:dyncon:v:52:y:2015:i:c:p:190-208
    DOI: 10.1016/j.jedc.2014.11.008
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    Cited by:

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    More about this item

    Keywords

    Partial information; Optimal monetary policy; Central bank preferences;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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